Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 59,633 bioRxiv papers from 265,294 authors.

Text search options

The Rxivist text search uses your query to search for relevant articles based on the article's title, abstract and list of authors. So a search for Smith will return any articles that include "Smith" in the title/abstract, or that include someone named "Smith" in the list of authors.

You can also use quotation marks (" or ') to indicate phrases, and the Boolean operators "AND" (&), "OR" (|) and "NOT" (!) within your search.

To find the information for a paper with a known DOI number, navigate to https://rxivist.org/papers/$DOI_NUMBER_HERE

Note: 18 Jul 2019 - Twitter data has been unavailable for several weeks; it will be automatically updated once the CrossRef API is functional again, but until then, the homepage will display download statistics instead.

Most downloaded bioRxiv papers, since beginning of last month

58,183 results found. For more information, click each entry to expand.

57541:Memory consolidation is linked to spindle-mediated information processing during sleep

How are brief encounters transformed into lasting memories? Previous research has established the role of non-rapid eye movement (NREM) sleep, along with its electrophysiological signatures of slow oscillations (SOs) and spindles, for memory consolidation. More recently, experimental manipulations have demonstrated that NREM sleep provides a window of opportunity to selectively strengthen particular memory traces via the delivery of sensory cues. It has remained unclear, however, whether experimental memory cueing triggers the brain's endogenous consolidation mechanisms (linked to SOs and/or spindles) and whether those mechanisms in turn mediate effective processing of the cue information. Here we devised a novel paradigm in which associative memories (adjective-object and adjective-scene pairs) were selectively cued during a post-learning nap, successfully stabilising next-day retention relative to non-cued memories. First, we found that compared to novel control adjectives, memory cues were accompanied by an increase in fast spindles coupled to SO up states. Critically, EEG pattern decodability of the associated memory category (object vs. scene) was temporally linked to cue-induced spindles and predicted next-day retrieval performance across participants. These results provide highly controlled empirical evidence for an information processing role of sleep spindles in service of memory consolidation.

The complexity of cancer signaling networks limits the efficacy of most single agent treatments and brings about challenges in identifying effective combinatorial therapies. Using chronic active B cell receptor (BCR) signaling in diffuse large B cell lymphoma (DLBCL) as a model system, we established a computational framework to optimize combinatorial therapy in silico. We constructed a detailed kinetic model of the BCR signaling network, which captured the known complex crosstalk between the NFκB, ERK and AKT pathways; and multiple feedback loops. Combining this signaling model with a data-derived tumor growth model we predicted viability responses of many single drug and drug combinations that are in agreement with experimental data. Under this framework, we exhaustively predicted and ranked the efficacy and synergism of all possible combinatorial inhibitions of eleven currently targetable kinases in the BCR signaling network. Our work established a detailed kinetic model of the core BCR signaling network and provides the means to explore the large space of possible drug combinations.

57543:Decrease of Nibrin expression in chronic hypoxia is associated with hypoxia-induced chemoresistance in medulloblastoma cells

Solid tumours are less oxygenated than normal tissues. This is called tumour hypoxia and leads to resistance to radiotherapy and chemotherapy. The molecular mechanisms underlying such resistance have been investigated in a range of tumour types, including the adult brain tumours glioblastoma, yet little is known for paediatric brain tumours. Medulloblastoma (MB) is the most common malignant brain tumour in children. Here we used a common MB cell line (D283-MED), to investigate the mechanisms of chemo and radio-resistance in MB, comparing to another MB cell line (MEB-Med8A) and to a widely used glioblastoma cell line (U87MG). In D283-MED and U87MG, chronic hypoxia (5 days), but not acute hypoxia (24 h) induced resistance to etoposide and X-ray irradiation. This acquired resistance upon chronic hypoxia was much less pronounced in MEB-Med8A cells. Using a transcriptomic approach in D283-MED cells, we found a large transcriptional remodelling upon long term hypoxia, in particular the expression of a number of genes involved in detection and repair of double strand breaks (DSB) was altered. The levels of Nibrin (NBN) and MRE11, members of the MRN complex (MRE11/Rad50/NBN) responsible for DSB recognition, were significantly down-regulated. This was associated with a reduction of Ataxia Telangiectasia Mutated (ATM) activation by etoposide, indicating a profound dampening of the DNA damage signalling in hypoxic conditions. As a consequence, p53 activation by etoposide was reduced, and cell survival enhanced. Whilst U87MG shared the same dampened p53 activity, upon chemotherapeutic drug treatment in chronic hypoxic conditions, these cells used a different mechanism, independent of the DNA damage pathway. Together our results demonstrate a new mechanism explaining hypoxia-induced resistance involving the alteration of the response to DSB, but also highlight the cell type to cell type diversity and the necessity to take into account the differing tumour genetic make-up when considering re-sensitisation therapeutic protocols.

Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of these systems emerge not only from the protein interactions themselves but also from the dependencies between these interactions, e.g., allosteric effects, mutual exclusion or steric hindrance. Therefore, formal models for integrating and using information about such dependencies are of high interest. We present an approach for endowing protein networks with interaction dependencies using propositional logic, thereby obtaining constrained protein interaction networks ("constrained networks"). The construction of these networks is based on public interaction databases and known as well as text-mined interaction dependencies. We present an efficient data structure and algorithm to simulate protein complex formation in constrained networks. The efficiency of the model allows a fast simulation and enables the analysis of many proteins in large networks. Therefore, we are able to simulate perturbation effects (knockout and overexpression of single or multiple proteins, changes of protein concentrations). We illustrate how our model can be used to analyze a partially constrained human adhesome network. Comparing complex formation under known dependencies against without dependencies, we find that interaction dependencies limit the resulting complex sizes. Further we demonstrate that our model enables us to investigate how the interplay of network topology and interaction dependencies influences the propagation of perturbation effects. Our simulation software CPINSim (for Constrained Protein Interaction Network Simulator) is available under the MIT license at http://github.com/BiancaStoecker/cpinsim and via Bioconda (https://bioconda.github.io).

Meta-analysis of multiple genome-wide association studies (GWAS) has become an effective approach for detecting single nucleotide polymorphism (SNP) associations with complex traits. However, it is difficult to integrate the readily accessible SNP-level summary statistics from a meta-analysis into more powerful multi-marker testing procedures, which generally require individual-level genetic data. We developed a general procedure called Summary based Adaptive Rank Truncated Product (sARTP) for conducting gene and pathway meta-analysis that uses only SNP-level summary statistics in combination with genotype correlation estimated from a panel of individual-level genetic data. We demonstrated the validity and power advantage of sARTP through empirical and simulated data. We conducted a comprehensive pathway-based meta-analysis with sARTP on type 2 diabetes (T2D) by integrating SNP-level summary statistics from two large studies consisting of 19,809 T2D cases and 111,181 controls with European ancestry. Among 4,713 candidate pathways from which genes in neighborhoods of 170 GWAS established T2D loci were excluded, we detected 43 T2D globally significant pathways (with Bonferroni corrected p-values < 0.05), which included the insulin signaling pathway and T2D pathway defined by KEGG, as well as the pathways defined according to specific gene expression patterns on pancreatic adenocarcinoma, hepatocellular carcinoma, and bladder carcinoma. Using summary data from 8 eastern Asian T2D GWAS with 6,952 cases and 11,865 controls, we showed 7 out of the 43 pathways identified in European populations remained to be significant in eastern Asians at the false discovery rate of 0.1. We created an R package and a web-based tool for sARTP with the capability to analyze pathways with thousands of genes and tens of thousands of SNPs.

57546:Method to estimate the approximate samples size that yield a certain number of significant GWAS signals in polygenic traits

To argue for increased sample collection for disorders without significant findings, researchers retorted to plotting, for multiple traits, the number of significant findings as a function of the sample size. However, for polygenic traits, the prevalence of the disorder confounds the relationship between the number of significant findings and the sample size. To adjust the number of significant findings for prevalence, we develop a method that uses the expected noncentrality of the contrast between liabilities of cases and controls. We empirically find that, when compared to the sample size, this measure is a better predictor of number of significant findings. Even more, we show that the sample size effect on the number of signals is explained by the noncetrality measure. Finally, we provide an R script to estimate the required sample size (non-centrality) needed to yield a pre-specified number of significant findings.

Anesthetized rodent models are ubiquitous in pre-clinical neuroimaging studies. However, because the associated cerebral morphology and experimental methodology results in a profound negative brain-core temperature differential, cerebral temperature changes during functional activation are likely to be principally driven by local inflow of fresh, core-temperature, blood. This presents a confound to the interpretation of blood-oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) data acquired from such models, since this signal is also critically temperature-dependent. Nevertheless, previous investigation on the subject is surprisingly sparse. Here, we address this issue through use of a novel multi-modal methodology in the urethane anesthetized rat. We reveal that sensory stimulation, hypercapnia and recurrent acute seizures induce significant increases in cortical temperature that are preferentially correlated to changes in total hemoglobin concentration, relative to cerebral blood flow and oxidative metabolism. Furthermore, using a phantom-based evaluation of the effect of such temperature changes on the BOLD fMRI signal, we demonstrate a robust inverse relationship between the two. These findings indicate that temperature increases, due to functional hyperemia, should be accounted for to ensure accurate interpretation of BOLD fMRI signals in pre-clinical neuroimaging studies.

57548:Unlocked capacity of proteins to attack membranes characteristic of aggregation: the evil for diseases and aging from Pandora's box

Aggregation of specific proteins is characteristic of a large spectrum of human diseases including all neurodegenerative diseases, while aggregation of non-specific proteins has been now identified to be a biomarker for cellular aging down to Escherichia coli. Previously, as facilitated with our discovery in 2005 that completely insoluble proteins could be all solubilized in unsalted water [Song (2009) FEBS Lett. 583: 953], we found that the TDP-43 prion-like domain in fact contains an intrinsic membrane-interacting subdomain [Lim et al. [2016] PLoS Biol. 14, e1002338]. We decrypted that ALS-causing mutations/cofactor-depletion act to render the well-structured folds of cytosolic VAPB-MSP domain and SOD1 into highly disordered states, thus becoming buffer-insoluble. Most surprisingly, this also unlocks the amphiphilic/hydrophobic regions universally exiting in proteins, which thus acquire a novel capacity in abnormally interacting with membranes [Qin et al. (2013) F1000Res 2-221.v2; Lim (2016) BBA-Biomembranes. 1858: 2223]. Here we aimed extend our discovery to address two fundamental questions: 1) why many E. coli proteins become aggregated in aging; and 2) whether aggregation-prone proteins can also acquire a novel capacity in interacting with membranes; by dissecting the 557-residue S1 ribosomal protein into 7 fragments to disrupt its 6 S1 folds, followed by extensive CD and NMR characterizations. The results reveal that we have successfully eliminated all 6 S1 folds and fragment 4 becomes highly disordered and thus buffer-insoluble. Most strikingly, F4 does acquire a capacity in transforming into a helical conformation in membrane environments. Here, for the first time, our study deciphers that like ALS-causing mutants, the disruption of a well-folded E. coli cytosolic protein also unlocks its amphiphilic/hydrophobic regions which are capable of abnormally interacting with membranes. Therefore, proteins, the most important functional players for all forms of life, can transform into membrane-toxic forms triggering diseases and aging, if their hydrophobic/amphiphilic regions are unlocked by genetic, pathological or/and environmental factors, which is characteristic of severe aggregation.

Cholera is a severe, waterborne diarrheal disease caused by toxin-producing strains of the bacterium Vibrio cholerae. Comparative genomics has revealed "waves" of cholera transmission and evolution, in which clones are successively replaced over decades and centuries. However, the extent of V. cholerae genetic diversity within an epidemic or even within an individual patient is poorly understood. Here, we characterized V. cholerae genomic diversity at a micro-epidemiological level within and between individual patients from Bangladesh and Haiti. To capture within-patient diversity, we isolated multiple (8 to 20) V. cholerae colonies from each of eight patients, sequenced their genomes and identified point mutations and gene gain/loss events. We found limited but detectable diversity at the level of point mutations within hosts (zero to three single nucleotide variants within each patient), and comparatively higher gene content variation within hosts (at least one gain/loss event per patient, and up to 103 events in one patient). Much of the gene content variation appeared to be due to gain and loss of phage and plasmids within the V. cholerae population, with occasional exchanges between V. cholerae and other members of the gut microbiota. We also show that certain intra-host variants have phenotypic consequences. For example, the acquisition of a Bacteroides plasmid and nonsynonymous mutations in a sensor histidine kinase gene both reduced biofilm formation, an important trait for environmental survival. Together, our results show that V. cholerae is measurably evolving within patients, with possible implications for disease outcomes and transmission dynamics.

RNA-seq data are challenging existing omics data analytics for its volume and complexity. Although quite a few computational models were proposed from different standing points to conduct differential expression (D.E.) analysis, almost all these methods do not provide a rigorous feature selection for high-dimensional RNA-seq count data. Instead, most or even all genes are invited into differential calls no matter they have real contributions to data variations or not. Thus, it would inevitably affect the robustness of D.E. analysis and lead to the increase of false positive ratios. In this study, we presented a novel feature selection method: nonnegative singular value approximation (NSVA) to enhance RNA-seq differential expression analysis by taking advantage of RNA-seq count data's non-negativity. As a variance-based feature selection method, it selects genes according to its contribution to the first singular value direction of input data in a data-driven approach. It demonstrates robustness to depth bias and gene length bias in feature selection in comparison with its five peer methods. Combining with state-of-the-art RNA-seq differential expression analysis, it contributes to enhancing differential expression analysis by lowering false discovery rates caused by the biases. Furthermore, we demonstrated the effectiveness of the proposed feature selection by proposing a data-driven differential expression analysis: NSVA-seq, besides conducting network marker discovery.

Humans have a remarkably high capacity and long duration memory for complex scenes. Previous research documents the neural substrates that allow for efficient categorization of scenes from other complex stimuli like objects and faces, but the spatiotemporal neural dynamics underlying scene memory are less well understood. In the present study, we used high density EEG during a visual continuous recognition task in which new, old, and scrambled scenes consisting of color outdoor photographs were presented at an average rate 0.26 Hz. Old scenes were single repeated presentations occurring within either a short-term (< 20 seconds) or longer-term intervals of between 30 sec and 3 minutes or 4 and 10 minutes. Overall recognition was far above chance, with better performance at short- than longer-term intervals. A group ANOVA found parietal and frontal ERPs discriminated the three scene types as early as 59 ms after stimulus onset. Parietal ERPs were greater for old compared to new scenes by 189 ms, while fronto-temporal ERPs were greater for new compared to old scenes by 194 ms. For old scenes presented within longer-term intervals, parieto-temporal and centro-frontal ERPs were greater by 228 and 355 ms respectively compared to old scenes presented within a short-term interval. Supervised machine learning exhibited above-chance decoding of scene type by 275 ms. Single-subject BOLD-fMRI showed greater activity for old scenes across frontal, parietal, and temporal cortex. These converging findings show that a widespread network including parietal, frontal, and temporal regions supports short- and long-term scene memory.

Background: Associations between dopamine receptor levels and pro- and antisocial behavior have previously been demonstrated in human subjects using positron emission tomography (PET) and self-rated measures of personality traits. So far, only one study has focused on the D1-dopamine receptor (D1-R), finding a positive correlation with the trait social desirability, which is characterized by low dominant and high affiliative behavior, while physical aggression showed a negative correlation. The aim of the present study was to replicate these previous findings using a new independent sample of subjects. Methods: Twenty-six healthy males were examined with the radioligand [11]SCH-23390, and completed the Swedish universities Scales of Personality (SSP) which includes measures of social desirability and physical trait aggression. The simplified reference tissue model with cerebellum as reference region was used to calculate BPND values in the whole striatum and limbic striatum. The two regions were selected since they showed strong association between D1-R availability and personality scores in the previous study. Pearson's correlation coefficients and replication Bayes factors were then employed to assess the replicability and robustness of previous results. Results: There were no significant correlations (all p values > 0.3) between regional BPND values and personality scale scores. Replication Bayes factors showed strong to moderate evidence in favor no relationship between D1-receptor availability and social desirability (striatum BF01 = 12.4; limbic striatum BF01 = 7.2) or physical aggression scale scores (limbic striatum BF01 = 3.3), compared to the original correlations. Discussion: We could not replicate the previous findings of associations between D1-R availability and either pro- or antisocial behavior as measured using the SSP. Rather, there was evidence in favor of failed replications of associations between BPND and scale scores. Potential reasons for these results are restrictive variance in both PET and personality outcomes due to high sample homogeneity, or that the previous findings were false positives.

Pancreatic carcinoma (PC) is the one of the most common and malignant cancer in the world. Despite many effort have been made in recent years, the survival rate of PC still remains unsatisfied. Therefore, investigating the mechanisms underlying the progression of PC might facilitate the development of novel treatments that improve patient prognosis. LncRNA Taurine Up-regulated Gene 1 (TUG1) was initially identified as a transcript up- regulated by taurine, siRNA-based depletion of TUG1 suppresses mouse retinal development, and the abnormal expression of TUG1 has been reported in many cancers. However, the biological role and molecular mechanism of TUG1 in pancreatic carcinoma (PC) still needs to be further investigated. In the current study, the expression of TUG1 in the PC cell lines and tissues was measured by quantitative real-time PCR (qRT-PCR), and loss-of-function and gain-of-function approaches were applied to investigate the function of TUG1 in PC cell. Online database analysis tools showed that miR-382 could interact with TUG1 and we found an inverse correlation between TUG1 and miR-382 in PC specimens. Moreover, dual luciferase reporter assay, RNA-binding protein immunoprecipitation (RIP) and applied biotin-avidin pulldown system further provide evidence that TUG1 directly targeted miR-382 by binding with microRNA binding site harboring in the TUG1 sequence. Furthermore, gene expression array analysis using clinical samples and RT-qPCR proposed that EZH2 was a target of miR-382 in PC. Collectively, these findings revealed that TUG1 functions as an oncogenic lncRNA that promotes tumor progression at least partially through function as an endogenous ‘sponge’ by competing for miR-382 binding to regulate the miRNA target EZH2.

Sex promotes the recombination and reassortment of genetic material and is prevalent across eukaryotes. In social amoebae sex involves a promiscuous mixing of cytoplasm before zygotes consume the majority of cells. We report here the first genomewide characterisation of meiotic progeny in Dictyostelium discoideum. We find that recombination occurs at high frequency in pairwise crosses between all three mating types, despite the absence of the SPO11 enzyme that is normally required to initiate crossover formation. In crosses involving three strains, transient fusions involving more than two gametes frequently lead to triparental inheritance, with recombined nuclear haplotypes inherited from two parents and the mitochondrial genome from a third. Cells that do not contribute genetically to the Dictyostelium zygote nucleus thereby have a stake in the next haploid generation. We suggest that this lateral transfer helps to enforce cooperation in this conflictual system.

57555:Model adequacy and the macroevolution of angiosperm functional traits

Making meaningful inferences from phylogenetic comparative data requires a meaningful model of trait evolution. It is thus important to determine whether the model is appropriate for the data and the question being addressed. One way to assess this is to ask whether the model provides a good statistical explanation for the variation in the data. To date, researchers have focused primarily on the explanatory power of a model relative to alternative models. Methods have been developed to assess the adequacy, or absolute explanatory power, of phylogenetic trait models but these have been restricted to specific models or questions. Here we present a general statistical framework for assessing the adequacy of phylogenetic trait models. We use our approach to evaluate the statistical performance of commonly used trait models on 337 comparative datasets covering three key Angiosperm functional traits. In general, the models we tested often provided poor statistical explanations for the evolution of these traits. This was true for many different groups and at many different scales. Whether such statistical inadequacy will qualitatively alter inferences draw from comparative datasets will depend on the context. Regardless, assessing model adequacy can provide interesting biological insights -- how and why a model fails to describe variation in a dataset gives us clues about what evolutionary processes may have driven trait evolution across time.

Adaptation in diploids is predicted to proceed via mutations that are at least partially dominant in fitness. Recently we argued that many adaptive mutations might also be commonly overdominant in fitness. Natural (directional) selection acting on overdominant mutations should drive them into the population but then, instead of bringing them to fixation, should maintain them as balanced polymorphisms via heterozygote advantage. If true, this would make adaptive evolution in sexual diploids differ drastically from that of haploids. Unfortunately, the validity of this prediction has not yet been tested experimentally. Here we performed 4 replicate evolutionary experiments with diploid yeast populations (Saccharomyces cerevisiae) growing in glucose-limited continuous cultures. We sequenced 24 evolved clones and identified initial adaptive mutations in all four chemostats. The first adaptive mutations in all four chemostats were three CNVs, all of which proved to be overdominant in fitness. The fact that fitness overdominant mutations were always the first step in independent adaptive walks strongly supports the prediction that heterozygote advantage can arise as a common outcome of directional selection in diploids and demonstrates that overdominance of de novo adaptive mutations in diploids is not rare.

57557:The functional and genetic associations of neuroimaging data: a toolbox

Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and to identify the roots of psychiatric diseases. However, the results generated by most neuroimaging studies, i.e., activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. We describe a Brain Annotation Toolbox (BAT), a toolbox that helps to generate functional and genetic annotations for neuroimaging results. The toolbox can take data from brain regions identified with an atlas, or from brain regions identified as activated in tasks, or from functional connectivity links or networks of links. Then, the voxel-level functional description from the Neurosynth database and the gene expression profile from the Allen Brain Atlas are used to generate functional and genetic knowledge for such region-level data. Parametric (Fisher's exact test) or non-parametric (permutation test) statistical tests are adopted to identify significantly related functional descriptors and genes for the neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and gene co-expression networks are highly correlated for many major brain atlases. One application of BAT is to help provide functional and genetic annotations for the newly discovered regions with unknown functions, e.g., the 97 new regions identified in the Human Connectome Project. Importantly too, this toolbox can help understand differences between patients with psychiatric disorders and controls, and this is demonstrated using data for schizophrenia and autism, for which the functional and genetic annotations for the neuroimaging data differences between patients and controls are consistent with each other and help with the interpretation of the differences.

South Korea shows a remarkable rapid increase in lifespan in recent decades. Employing a mathematical model that is appropriate for human survival curves, we evaluate current trends in female lifespan for South Korea over three recent decades, 1987-2016, and predict coming trends in female lifespan until 2030. From comparative analyses with industrialized countries such as Japan, France, Australia, Switzerland, UK, Sweden, and USA, we confirm that South Korea has the highest increase rate of female lifespan in recent decades, and estimate that maximum lifespan would reach 125 years and characteristic life would surpass 95 years for South Korean female by 2030. South Korea would deserve much attention in study on human health and longevity as the longest-lived country in coming decades.

Motivation: The accurate description of interfaces is needed to identify which residues interact with another molecule or macromolecule. In addition, a data structure is required to compare interfaces within or between families of protein-protein or protein-ligands complexes. In order to avoid many unwanted comparisons, we looked for a parameter free computation of interfaces. This need appeared at the occasion of bioinformatics studies by our research team focusing on HIV-2 protease (PR2) resistance to its inhibitors. Results: We designed the PPIC software (Protein Protein Interface Computation). It offers three methods of computation of interfaces: (1) our original parameter free method, (2) the Voronoi tessellation approach, and (3) the cutoff distance method. For the latter, we suggest on the basis of 1050 dimers protein-protein interfaces that the optimal cutoff distance is 3.7 Å, or 3.6 Å for a set of 18 PR2-ligand interfaces. We found at most 17 contact residues with PR2 ligands. Availability: Free binaries and documentation are available through a software repository located at http://petitjeanmichel.free.fr/itoweb.petitjean.freeware.html

57560:The modulation of neural gain facilitates a transition between functional segregation and integration in the brain

Cognitive function relies on a dynamic, context-sensitive balance between functional integration and segregation in the brain. Previous work has proposed that this balance is mediated by global fluctuations in neural gain by projections from ascending neuromodulatory nuclei. To test this hypothesis in silico, we studied the effects of neural gain on network dynamics in a model of large-scale neuronal dynamics. We found that increases in neural gain pushed the network through an abrupt dynamical transition, leading to an integrated network topology that was maximal in frontoparietal ‘rich club’ regions. This gain-mediated transition was also associated with increased topological complexity, as well as increased variability in time-resolved topological structure, further highlighting the potential computational benefits of the gain-mediated network transition. These results support the hypothesis that neural gain modulation has the computational capacity to mediate the balance between integration and segregation in the brain.